Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings

Abstract

Point clouds provide a novel and effective alternative to understanding the structural behaviours of segmental tunnel linings. 3D deep learning (DL) has emerged as a promising technology capable of automatically deriving point-wise semantic and instance labels from point clouds. The utilisation of 3D DL in segment segmentation of tunnel point clouds has not been explored and the development of tailored 3D DL networks has been hindered by the absence of specialised datasets and benchmarks. To bridge this gap, this paper introduces a richly annotated hierarchical dataset: ‘Seg2Tunnel’, acquired from five tunnels and including 1,300 tunnel rings. Using the Seg2Tunnel dataset, the feasibility of applying 3D DL to the segment segmentation is demonstrated for the first time. Experiments are conducted to investigate the influences of training set size, data augmentation strategy, input size, and hyperparameter on the performance of trained 3D DL models and to provide benchmarks and insights for future uses of the Seg2Tunnel dataset. The 3D DL models trained by the Seg2Tunnel dataset outperform currently existing image- and voxel-based DL methods. The Seg2Tunnel dataset and benchmarks are fundamental in shaping the design of 3D DL networks tailored for tunnel point clouds. The study provides a novel paradigm for automatically understanding the tunnel structural elements in the point clouds, paving the way for unmanned construction and intelligent evaluation of segmental tunnel linings.